Title :
Multiobjective optimization method based on a genetic algorithm for switched reluctance motor design
Author :
Mirzaeian, B. ; Moallem, M. ; Tahani, V. ; Lucas, C.
Author_Institution :
Dept. of Electr. Eng., Isfahan Univ. of Technol., Iran
fDate :
5/1/2002 12:00:00 AM
Abstract :
In this paper, a novel multiobjective optimization method based on a genetic-fuzzy algorithm (GFA) is proposed. The new GFA method is used for optimal design of a switched reluctance motor (SRM) with two objective functions: high efficiency and low torque ripple. The results of the optimal design for an 8/6, four-phase, 4 kW, 250 V, 1500 r.p.m. SRM show improvement in both efficiency and torque ripple of the motor
Keywords :
electric machine CAD; expert systems; fuzzy systems; genetic algorithms; machine theory; reluctance motors; torque; 250 V; 4 kW; SRM design; four-phase SRM; fuzzy expert system; genetic-fuzzy algorithm; high efficiency; low torque ripple; multiobjective optimization method; objective functions; optimal design; switched reluctance motor; Algorithm design and analysis; Biological cells; Design optimization; Equations; Genetic algorithms; Genetic mutations; Optimization methods; Reluctance machines; Reluctance motors; Torque;
Journal_Title :
Magnetics, IEEE Transactions on
